Using Data to Improve Learning Arlington County Public Schools October 6, 2014 Denny Berry, Ed.D. dberry@virginia.edu 703-536-1136

Check-In – At Your Tables What does the word “data” bring to mind for you?

Essential Learning Refine understanding of the purpose of using data in a professional learning community culture. Provide time to dialogue, discuss, and reflect.

Clarity Precedes Competence

PLC Defined Educators committed to working collaboratively in recurring processes of collective inquiry and action research in order to achieve better results for the students they serve. PLC’s operate under the cultural assumption that the key to improved learning for students is continuous, job-embedded learning for educators.

The Three Big Ideas in a Professional Learning Community Culture

The problem with unstructured meetings in a PLC “Professional learning communities can offer significant benefits, but only if they are explicitly focused on analyzing student learning and identifying key action steps based on that analysis . The original definition of the professional learning communities was intended to do just that; too many schools get off track.” Bambrick-Santoyo (2010). Driven by data: A practical guide to improve instruction. San Francisco: Jossey-Bass, pg. xxxiii

Clarity Why do we want to do this? What does this mean, and what might it look like in practice? How do we do this?

Assumption #1 Making significant progress in improving student learning and closing achievement gaps is a moral responsibility and a real possibility in a relatively short amount of time—two to five years. It is not children’s poverty or race or ethnic background that stands in the way of achievement; it is school practices and policies and the beliefs that underlie them that pose the biggest obstacles.

Assumption #2 Data have no meaning. Meaning is imposed through interpretation. Conversely, data themselves can also be a catalyst to questioning assumptions and changing practices based on new ways of thinking.

Assumption #3 Collaborative inquiry—a process where teachers construct their understanding of student-learning problems and invent and test out solutions together through rigorous and frequent use of data and reflective dialogue—unleashes the resourcefulness and creativity to continuously improve instruction and student learning.

Assumption #4 A school culture characterized by collective responsibility for student learning, commitment to equity, and trust is the foundation for collaborative inquiry.

Assumption #5 Using data itself does not improve teaching. Improved teaching comes about when teachers implement sound teaching practices grounded in cultural proficiency—understanding and respect for their students’ cultures—and a thorough understanding of the subject matter and how to teach it.

Assumption #6 Every member of a collaborative school community (a professional learning community) can act as a leader, dramatically impacting the quality of relationships, the school culture, and student learning.

Say Something With a partner: Read to the stopping place, and “say something” about the text. Continue reading to the end of the article, and again “say something.” Be prepared to share out: What are the big ideas?

At your table: What assessments do you have that provide data? What are other sources of data?

Summative Formative State Assessments Demographics Benchmarks Common Assessments Classroom Assessments Formative

The Data Divide Imagine two shores with an ocean in between. On one shore are data—the variety of data we now have. On the other shore is our goal to improve student learning. But often there is an ocean and no bridge in between. And some children are drowning. The Using Data Process is about building the bridge between the data that we have and the results that we want. A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.

What successful data practices are you using What successful data practices are you using? What do you think creates the bridge between data and results? What is the bridge made of?

Core Value Collaborative inquiry—school teams constructing meaning of student-learning problems and testing out solutions together through rigorous use of data and reflective dialogue—unleashes the resourcefulness of educators to continuously improve instruction and student learning. This is a second underlying value of the Using Data Process. [Read slide] A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.

Building the Bridge Between Data and Results To return to our bridge metaphor: Collaborative inquiry is the bridge—the process that connects data to results. The first step across the bridge is to build the leadership and capacity of Data Coaches and Data Teams to use data effectively. The second step is collaboration, where Data Teams meet regularly and follow a structured improvement process such as the Using Data Process. They use their time productively to improve teaching and learning. The third step is frequent and in-depth use of multiple data sources, including common assessments at the item level and analysis of student work. The final and necessary step to improve results is instructional improvement as a result of teachers sharing their successes, setting goals for improvement, changing their teaching, and monitoring their results. All of this rests on the foundation of a collaborative culture based on a shared commitment to equity and trust. Leadership & Capacity Collaboration Data Use Instructional Improvement A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.

Shifts That Are Evident in Using Data Schools Less Emphasis More Emphasis External accountability, cultural blindness, little trust Internal and collective responsibility,cultural proficiency, trust Culture, Equity, Trust This slide summarizes the shifts the Using Data Process will help bring about. Here are some quotes from participants in the UDP that illustrate these shifts [read shift first and then quotation]: Culture: “When people here say ‘data,’ they usually think of that stuff they take care of in the office. Through the UDP, we learn that we work together to analyze the data and that there are direct implications for classroom instruction. There is something that everyone can do to have all of our students be the best they can be.” Instructional improvement: “We learned that we needed to look at what we could do as a staff that would have the most impact on students—instructional practice.” Data use: “We used to look at data but never understood that the power was in the details—the item analysis and student work.” Collaboration: “Teachers used to stand outside and wait for kids. They are now walking down the hall to talk to other teachers.” Leadership and capacity: “With teachers as the change agents, we are starting to see real movement.” Instructional Improvement Data to sort, learning left to chance Data to serve, expanding opportunities for all Feedback for continuous improvement, frequent and in-depth use by teachers and students Data Use Punishment/reward, avoidance Top-down, data-driven decision making Collaboration Ongoing Data-Driven Dialogue and collaborative inquiry Individual charismatic leaders as change agents Learning communities with many change agents Leadership & Capacity A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.

3. Rate your own skills in leading data conversations. To what degree do you believe in the need to use data to help improve instruction? ( 5 – 4 – 3 – 2 – 1) How frequently do teachers work together to examine data and reflect together about teaching and learning? (5-daily, 4-weekly, 3-quarterly, 2-beginning & end of year, 1-never)? 3. Rate your own skills in leading data conversations. (5 – 4 – 3 – 2 – 1 ) 4. Rate your own skills as a participant in data conversations. (5 – 4 – 3 – 2 – 1)

A Data Conversation or Dialogue Predict: Activating prior knowledge and surfacing assumptions Go Visual: Using vibrant visual displays Observe: Opening up extended opportunities for exploration and discovery Infer/Question: Generating possible explanations, inferences, and questions about the data

Identifying a Student Learning Problem Drill down into multiple levels and sources of student-learning data using an agreed upon protocol. Synthesize findings. Formulate a student-learning problem statement and goal on which to focus improvement.

Level of Data SOL Common Assessment: Performance Task District Benchmark Aggregated 52% of 6th graders were proficient in science, a 2% decrease from last year 65% of all 6th graders scored a rubric rating of 2 or below 60% of all 6th graders passed the physical science assessment Disaggregated Persistent gap between Hispanic and White students; this year’s gap was 38% Of the students scoring 2 or below, 70% were Hispanic, 52% were White A gap of 28% between Hispanic and White students Strand For two years, the lowest % of students were proficient in the physical science strand ; 31% this year and 28% last year NA – the assessment focused on one strand Item Analysis Of the eight physical science items, students performed worst on the three items pertaining to floating and sinking; 23-30% proficient Students performed poorly on the part of the task that required them to explain why an object floated or sank Students performed poorly on the 10 items assessing buoyancy with an average of 22% Student Work NA Showed evidence of student misconceptions; relied on the object’s size to determine buoyancy Showed evidence of misconceptions with the concept of buoyancy and how the composition of an object relates to its buoyancy

The collaborative data inquiry cycle “It takes a culture---a learning driven school in which professionals are constantly asking one another the kinds of questions that challenge existing beliefs and practices and lead to new learning. That’s what a true professional learning community is about.” Katz, S. & Dack, S. (2013). Intentional interruptions: Breaking down barriers to transform professional practice. Thousand Oaks, CA: Corwin.

References Bambrick-Santoyo, P. (2010). Driven by data: A practical guide to improve instruction. San Francisco: Jossey-Bass Katz, S. & Dack, S. (2013). Intentional interruptions: Breaking down barriers to transform professional practice. Thousand Oaks, CA: Corwin. Love, N. (2009). Using data to improve learning for all: A collaborative inquiry approach. Thousand Oaks, CA: Corwin.